• Journal of Genetic Medicine
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• v.12 no.1
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• pp.25-30
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• 2015

Purpose: Charcot-Marie-Tooth disease (CMT) is a peripheral neuropathy mainly divided into CMT type 1 (CMT1) and CMT2 according to the phenotype and genotype. Although molecular pathologies for each genetic causative have not been revealed in CMT2, the correlation between cell death and accumulation of misfolded proteins in the endoplasmic reticulum (ER) of Schwann cells is well documented in CMT1. Establishment of in vitro models of ER stress-mediated Schwann cell death might be useful in developing drug-screening systems for the treatment of CMT1. Materials and Methods: To develop high-throughput screening (HTS) systems for CMT1, we generated cell models using transient expression of mutant proteins and chemical induction. Results: Overexpression of wild type and mutant peripheral myelin protein 22 (PMP22) induced ER stress. Similar results were obtained from mutant myelin protein zero (MPZ) proteins. Protein localization revealed that expressed mutant PMP22 and MPZ proteins accumulated in the ER of Schwann cells. Overexpression of wild type and L16P mutant PMP22 also reduced cell viability, implying protein accumulation-mediated ER stress causes cell death. To develop more stable screening systems, we mimicked the ER stress-mediated cell death in Schwann cells using ER stress inducing chemicals. Thapsigargin treatment caused cell death via ER stress in a dose dependent manner, which was measured by expression of ER stress markers. Conclusion: We have developed genetically and chemically induced ER stress models using Schwann cells. Application of these models to HTS systems might facilitate the elucidation of molecular pathology and development of therapeutic options for CMT1.

• BMB Reports
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• v.56 no.3
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• pp.178-183
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• 2023

Huntington's disease (HD) is a neurodegenerative disorder, of which pathogenesis is caused by a polyglutamine expansion in the amino-terminus of huntingtin gene that resulted in the aggregation of mutant HTT proteins. HD is characterized by progressive motor dysfunction, cognitive impairment and neuropsychiatric disturbances. Histone deacetylase 6 (HDAC6), a microtubule-associated deacetylase, has been shown to induce transport- and release-defect phenotypes in HD models, whilst treatment with HDAC6 inhibitors ameliorates the phenotypic effects of HD by increasing the levels of α-tubulin acetylation, as well as decreasing the accumulation of mutant huntingtin (mHTT) aggregates, suggesting HDAC6 inhibitor as a HD therapeutics. In this study, we employed in vitro neural stem cell (NSC) model and in vivo YAC128 transgenic (TG) mouse model of HD to test the effect of a novel HDAC6 selective inhibitor, CKD-504, developed by Chong Kun Dang (CKD Pharmaceutical Corp., Korea). We found that treatment of CKD-504 increased tubulin acetylation, microtubule stabilization, axonal transport, and the decrease of mutant huntingtin protein in vitro. From in vivo study, we observed CKD-504 improved the pathology of Huntington's disease: alleviated behavioral deficits, increased axonal transport and number of neurons, restored synaptic function in corticostriatal (CS) circuit, reduced mHTT accumulation, inflammation and tau hyperphosphorylation in YAC128 TG mouse model. These novel results highlight CKD-504 as a potential therapeutic strategy in HD.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.2924-2944
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• 2023

Alzheimer's disease (AD) is a neurological condition that is recognized as one of the primary causes of memory loss. AD currently has no cure. Therefore, the need to develop an efficient model with high precision for timely detection of the disease is very essential. When AD is detected early, treatment would be most likely successful. The most often utilized indicators for AD identification are the Mini-mental state examination (MMSE), and the clinical dementia. However, the use of these indicators as ground truth marking could be imprecise for AD detection. Researchers have proposed several computer-aided frameworks and lately, the supervised model is mostly used. In this study, we propose a novel 3D Convolutional Neural Network Multilayer Perceptron (3D CNN-MLP) based model for AD classification. The model uses Attention Mechanism to automatically extract relevant features from Magnetic Resonance Images (MRI) to generate probability maps which serves as input for the MLP classifier. Three MRI scan categories were considered, thus AD dementia patients, Mild Cognitive Impairment patients (MCI), and Normal Control (NC) or healthy patients. The performance of the model is assessed by comparing basic CNN, VGG16, DenseNet models, and other state of the art works. The models were adjusted to fit the 3D images before the comparison was done. Our model exhibited excellent classification performance, with an accuracy of 91.27% for AD and NC, 80.85% for MCI and NC, and 87.34% for AD and MCI.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• Journal of agricultural medicine and community health
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• v.45 no.1
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• pp.13-40
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• 2020

Objectives: The purpose of this manuscript was to propose the policy and perspectives of prevention and management for hypertension and diabetes in Korea. Methods: Authors reviewed the chronic disease prevention and management projects and models were executed in Korea until now, and analyzed and evaluated their performances. Results: In the circumstances of Korea, the following several requisites should be improved ; Specific Korean strategy for development and pursuing of national level policy agenda for chronic disease management must be established. There are a need to establish several means of supplementing the weaknesses of the current chronic disease management policies and programs. Firstly, development and distribution of contents of guidelines on the systematic project execution regime (regarding systematization of local community, subjects and contents of the projects) with guarantee for the quality of chronic disease prevention and management are necessary. Secondly, there is a need for development of information system that can lead the chronic disease management programs currently being implemented. Thirdly, there is urgent need to develop resources such as cultivation of manpower and facilities for provision of education and consultation for the patients and holders of risk factors of chronic disease. Fourthly, there is a need for means of securing management system and financial resources for operation of policies and programs. Conclusions: The results can be able to use as a road map, models, and direction and strategies of policies for chronic disease prevention and management of Korea.

• Korean Journal Plant Pathology
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• v.3 no.2
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• pp.131-136
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• 1987

Rates of lesion development over time and disease gradients over distance for blast disease on the two rice varieties, Brazos and M-20 1 were significantly affected by two different cultural conditions, upland and flooded conditions. Flooding rice field plots lowered the rates of lesion increase and flattened the disease gradients for both varieties. Despite absence of statistically significant differences in the rate of lesion increase between four sampled distances from infection focus, rate of lesion development tended to be slightly greater as distance from the infection focus increases. Rate of lesion increase was greater with more susceptible variety M-201 than with Brazos. Disease gradient was steeper for M-201 than for Brazos. As blast disease progressed, disease gradients became flattened regardless of variety due to the infections originated from secondary foci. Between two empirical disease gradient models examined, Kiyosawa & Shiyomi model was fitted better over Gregory model. Rates of blast isopath movement under upland conditions were calculated as approximately 0.2m/day and 0.4 m/day for Brazos and M-201, respectively. The results in this study suggest that differences in varietal resistance to blast could be detected by measuring disease gradient as efficiently as by measuring infection rate.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.9
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• pp.5483-5487
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• 2013

Background: Associations between Arg399Gln, Arg194Trp and Arg280His polymorphisms of the XRCC1 gene and risk of differentiated thyroid carcinoma (DTC) have been widely studied but the findings are contradictory. Methods: We performed a meta-analysis in the present study using STATA 11.0 software to clarify any associations. Electronic literature databases and reference lists of relevant articles revealed a total of 10, 6 and 6 published studies for the Arg399Gln, Arg194Trp and Arg280His polymorphisms, respectively. Results: No significant associations were observed between Arg399Gln and DTC risk in all genetic models within the overall and subgroup meta-analyses, while the Trp/Trp vs Arg/Arg and recessive model of the Arg194Trp polymorphism was associated with DTC susceptibility, and the dominant model of Arg280His polymorphism contributed to DTC susceptibility in Caucasians. Conclusions: Our meta-analysis suggests that XRCC1 Arg194Trp may be a risk factor for DTC development.

• Genomics & Informatics
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• v.16 no.4
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• pp.26.1-26.12
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• 2018

Shigella spp. constitutes some of the key pathogens responsible for the global burden of diarrhoeal disease. With over 164 million reported cases per annum, shigellosis accounts for 1.1 million deaths each year. Majority of these cases occur among the children of the developing nations and the emergence of multi-drug resistance Shigella strains in clinical isolates demands the development of better/new drugs against this pathogen. The genome of Shigella flexneri was extensively analyzed and found 4,362 proteins among which the functions of 674 proteins, termed as hypothetical proteins (HPs) had not been previously elucidated. Amino acid sequences of all these 674 HPs were studied and the functions of a total of 39 HPs have been assigned with high level of confidence. Here we have utilized a combination of the latest versions of databases to assign the precise function of HPs for which no experimental information is available. These HPs were found to belong to various classes of proteins such as enzymes, binding proteins, signal transducers, lipoprotein, transporters, virulence and other proteins. Evaluation of the performance of the various computational tools conducted using receiver operating characteristic curve analysis and a resoundingly high average accuracy of 93.6% were obtained. Our comprehensive analysis will help to gain greater understanding for the development of many novel potential therapeutic interventions to defeat Shigella infection.

• The Korean Journal of Physiology and Pharmacology
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• v.27 no.4
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• pp.333-344
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• 2023

Hepatocellular carcinoma (HCC) is a prevalent malignant tumor with high fatality. It has yet to be reported whether circ-SNX27 can affect the progression of HCC. This study attempted to analyze circ-SNX27's precise role and underlying mechanisms in HCC. HCC cell lines and tumor specimens from HCC patients were analyzed using quantitative real-time PCR and Western blotting to quantify the expressions of circ-SNX27, miR-375, and ribophorin I (RPN1). Cell invasion and cell counting kit 8 experiments were conducted for the evaluation of HCC cell invasion and proliferation. Caspase-3 Activity Assay Kit was utilized to gauge the caspase-3 activity. Luciferase reporter and RNA immunoprecipitation assays were executed to ascertain the relationships among miR-375, circ-SNX27, and RPN1. To determine how circ-SNX27 knockdown affects the growth of HCC xenografts in vivo, tumor-bearing mouse models were constructed. Elevated expressions of circ-SNX27 and RPN1 as well as a reduced miR-375 expression were observed among HCC cells and HCC patient tumor specimens. Knocking-down circ-SNX27 in HCC cells abated their proliferative and invasive abilities but raised their caspase-3 activity. Moreover, the poor levels of circ-SNX27 inhibited HCC tumor growth among the mice. Circ-SNX27 enhanced RPN1 by competitively binding with miR-375. Silencing miR-375 in HCC cells promoted their malignant phenotypes. Nonetheless, the promotive effect of miR375 silencing was reversible via the knockdown of circ-SNX27 or RPN1. This research demonstrated that circ-SNX27 accelerated the progression of HCC by modulating the miR-375/RPN1 axis. This is indicative of circ-SNX27's potential as a target for the treatment of HCC.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.111-124
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• 2018

This study aims to develop a classification model for predicting the occurrence of hyperlipidemia, one of the chronic diseases. Prior studies applying data mining techniques for predicting disease can be classified into a model design study for predicting cardiovascular disease and a study comparing disease prediction research results. In the case of foreign literatures, studies predicting cardiovascular disease were predominant in predicting disease using data mining techniques. Although domestic studies were not much different from those of foreign countries, studies focusing on hypertension and diabetes were mainly conducted. Since hypertension and diabetes as well as chronic diseases, hyperlipidemia, are also of high importance, this study selected hyperlipidemia as the disease to be analyzed. We also developed a model for predicting hyperlipidemia using SVM and meta learning algorithms, which are already known to have excellent predictive power. In order to achieve the purpose of this study, we used data set from Korea Health Panel 2012. The Korean Health Panel produces basic data on the level of health expenditure, health level and health behavior, and has conducted an annual survey since 2008. In this study, 1,088 patients with hyperlipidemia were randomly selected from the hospitalized, outpatient, emergency, and chronic disease data of the Korean Health Panel in 2012, and 1,088 nonpatients were also randomly extracted. A total of 2,176 people were selected for the study. Three methods were used to select input variables for predicting hyperlipidemia. First, stepwise method was performed using logistic regression. Among the 17 variables, the categorical variables(except for length of smoking) are expressed as dummy variables, which are assumed to be separate variables on the basis of the reference group, and these variables were analyzed. Six variables (age, BMI, education level, marital status, smoking status, gender) excluding income level and smoking period were selected based on significance level 0.1. Second, C4.5 as a decision tree algorithm is used. The significant input variables were age, smoking status, and education level. Finally, C4.5 as a decision tree algorithm is used. In SVM, the input variables selected by genetic algorithms consisted of 6 variables such as age, marital status, education level, economic activity, smoking period, and physical activity status, and the input variables selected by genetic algorithms in artificial neural network consist of 3 variables such as age, marital status, and education level. Based on the selected parameters, we compared SVM, meta learning algorithm and other prediction models for hyperlipidemia patients, and compared the classification performances using TP rate and precision. The main results of the analysis are as follows. First, the accuracy of the SVM was 88.4% and the accuracy of the artificial neural network was 86.7%. Second, the accuracy of classification models using the selected input variables through stepwise method was slightly higher than that of classification models using the whole variables. Third, the precision of artificial neural network was higher than that of SVM when only three variables as input variables were selected by decision trees. As a result of classification models based on the input variables selected through the genetic algorithm, classification accuracy of SVM was 88.5% and that of artificial neural network was 87.9%. Finally, this study indicated that stacking as the meta learning algorithm proposed in this study, has the best performance when it uses the predicted outputs of SVM and MLP as input variables of SVM, which is a meta classifier. The purpose of this study was to predict hyperlipidemia, one of the representative chronic diseases. To do this, we used SVM and meta-learning algorithms, which is known to have high accuracy. As a result, the accuracy of classification of hyperlipidemia in the stacking as a meta learner was higher than other meta-learning algorithms. However, the predictive performance of the meta-learning algorithm proposed in this study is the same as that of SVM with the best performance (88.6%) among the single models. The limitations of this study are as follows. First, various variable selection methods were tried, but most variables used in the study were categorical dummy variables. In the case with a large number of categorical variables, the results may be different if continuous variables are used because the model can be better suited to categorical variables such as decision trees than general models such as neural networks. Despite these limitations, this study has significance in predicting hyperlipidemia with hybrid models such as met learning algorithms which have not been studied previously. It can be said that the result of improving the model accuracy by applying various variable selection techniques is meaningful. In addition, it is expected that our proposed model will be effective for the prevention and management of hyperlipidemia.